"In recent years the analysis of data streams has received a lot of attention.. This is motivated by the increase of the number of applications which generate huge. amounts of high speed temporal data. Let us think to sensor networks, computer networks,. manufactures. Data streams are usually highly evolving, thus mining changes. in data is a challenging task. In this paper we will deal with the structural drift detection. problem where the aim is to discover and to describe changes in proximity. relations among multiple data streams. We will introduce a new strategy whose effectiveness. is shown through an application on simulated data."
Summarizing and Detecting Structural Drifts from Multiple Data Streams
BALZANELLA, Antonio;VERDE, Rosanna
2013
Abstract
"In recent years the analysis of data streams has received a lot of attention.. This is motivated by the increase of the number of applications which generate huge. amounts of high speed temporal data. Let us think to sensor networks, computer networks,. manufactures. Data streams are usually highly evolving, thus mining changes. in data is a challenging task. In this paper we will deal with the structural drift detection. problem where the aim is to discover and to describe changes in proximity. relations among multiple data streams. We will introduce a new strategy whose effectiveness. is shown through an application on simulated data."I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
